A New Approach to Predicting Analyst Forecast Errors: Do Investors Overweight Analyst Forecasts?

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: June 13, 2012

Abstract

I provide evidence that investors systematically overweight analyst forecasts by demonstrating that prices do not fully reflect the predictable component of analyst forecast errors. This evidence conflicts with conclusions in prior research relying on traditional approaches to predicting analyst errors. I highlight estimation bias associated with traditional approaches and develop a new approach that reduces this bias by directly forecasting future earnings. I estimate 'characteristic forecasts' using large sample relations to map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts future analyst forecast errors, forecast revisions, and changes in buy/sell recommendations. I document abnormal returns to a strategy that sorts firms based on predicted forecast errors, consistent with investors overweighting analyst forecasts relative to optimal Bayesian weights. Overweighting varies intuitively with characteristics of the information environment and across investor sentiment regimes. Taken together, the evidence suggests that predictable biases in analyst forecasts influence the information content of prices.